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An Artificial Immune Network for Multi-objective Optimization

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Artificial Neural Networks – ICANN 2010 (ICANN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6353))

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Abstract

This paper presents a method for approximating the Pareto front of a given function using Artificial Immune Networks. The proposed algorithm uses cloning and mutation to create local subsets of the Pareto front, and combines elements of these local fronts in a way that maximizes the diversity. The method is compared against SPEA and NSGA-II in a number of problems from the ZDT test suite, yielding satisfactory results.

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References

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Author information

Authors and Affiliations

  1. Intelligent Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780, Zografou, Athens, Greece

    Aris Lanaridis & Andreas Stafylopatis

Authors
  1. Aris Lanaridis
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  2. Andreas Stafylopatis
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Editor information

Editors and Affiliations

  1. Department of Informatics, TEI of Thessaloniki, 57400, Sindos, Greece

    Konstantinos Diamantaras

  2. School of Physics, Astronomy, and Informatics, Department of Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun, Poland

    Wlodek Duch

  3. Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Pantazidou 193, 68200, Orestiada Thrace, Greece

    Lazaros S. Iliadis

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Lanaridis, A., Stafylopatis, A. (2010). An Artificial Immune Network for Multi-objective Optimization. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15822-3_65

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  • DOI: https://doi.org/10.1007/978-3-642-15822-3_65

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15821-6

  • Online ISBN: 978-3-642-15822-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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